Computers and Concrete

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Applied linear and nonlinear statistical models for evaluating strength of Geopolymer concrete

  • Prem, Prabhat Ranjan (CSIR-Structural Engineering Research Centre) ;
  • Thirumalaiselvi, A. (CSIR-Structural Engineering Research Centre) ;
  • Verma, Mohit (CSIR-Structural Engineering Research Centre)
  • Received : 2018.11.05
  • Accepted : 2019.04.30
  • Published : 2019.07.25
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Abstract

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

Keywords

References

  1. Al-Rousan, R.Z., Alhassan, M.A. and Hejazi, M.A. (2018), "Novel nonlinear stiffness parameters and constitutive curves for concrete", Comput. Concrete, 22(6), 539-550. https://doi.org/10.12989/cac.2018.22.6.539.
  2. Ambily, P.S., Ravisankar, K., Umarani, C., Dattatreya, J.K. and Iyer, N.R. (2014), "Development of ultra-high-performance geopolymer concrete", Mag. Concrete Res., 66(2), 82-89. http://dx.doi.org/10.1680/macr.13.00057
  3. Arani, K.S., Zandi, Y., Pham, B.T., Mu'azu, M., Katebi, J., Mohammadhassani, M., Khala, S., Mohamad, E.T., Wakil, K. and Khorami, M. (2019), "Computational optimized nite element modelling of mechanical interaction of concrete with fiber reinforced polymer", Comput. Concrete, 23(1), 61-68. http://dx.doi.org/10.12989/cac.2019.23.1.061.
  4. Atis, C., Gorur, E., Karahan, O., Bilim, C., Ilkentapar, S. and Luga, E. (2015), "Very high strength (120mpa) class F fly ash geopolymer mortar activated at different NaOH amount, heat curing temperature and heat curing duration", Constr. Build. Mater., 96, 673-678. https://doi.org/10.1016/j.conbuildmat.2015.08.089.
  5. Bakharev, T. (2005), "Geopolymeric materials prepared using class f fly ash and elevated temperature curing", Cement Concrete Res., 35(6), 1224-1232. https://doi.org/10.1016/j.cemconres.2004.06.031.
  6. Breiman, L. (1996), "Bagging predictors", Mach. Learn., 24(2), 123-140. https://doi.org/10.1007/BF00058655.
  7. Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1993), "Classification and regression trees, wadsworth international group, belmont, ca, 1984" Case Description Feature Subset Correct Missed FA Misclass, 1, 1-3.
  8. Camps-Valls, G., Bruzzone, L., Rojo-Alvarez, J.L. and Melgani, F. (2006), "Robust support vector regression for biophysical variable estimation from remotely sensed images", Geosci. Remote Sens. Lett., IEEE, 3(3), 339-343. https://doi.org/10.1109/LGRS.2006.871748.
  9. Camps-Valls, G., Gmez-Chova, L., Muoz-Mar, J., Lzaro-Gredilla, M. and Ver relst, J. (2013), "simpleR: A simple educational Matlab toolbox for statistical regression", V2.1, http://www.uv.es/gcamps/code/simpleR.html.
  10. Camps-Valls, G., Munoz-Mari, J., Gomez-Chova, L., Guanter, L. and Calbet, X. (2012), "Nonlinear statistical retrieval of atmospheric profiles from metop-iasi and mtg-irs infrared sounding data", Geosci. Remote Sens., IEEE Tran. on, 50(5), 1759-1769. https://doi.org/10.1109/TGRS.2011.2168963
  11. Cundi, W., Hirano, Y., Terai, T., Vallepu, R., Mikuni, A. and Ikeda, K. (2005), "Preparation of geopolymeric monoliths from red mud-pfbc ash fillers at ambient temperature", Proc. World Cong. Geopolymer, Saint-Quentin, Franca, 85-87.
  12. Davidovits, J. (1991), "Geopolymers", J. Therm. Anal., 37(8), 1633-1656. https://doi.org/10.1007/BF01912193
  13. Detphan, S. and Chindaprasirt, P. (2009), "Preparation of fly ash and rice husk ash geopolymer", Int. J. Min. Metal. Mater., 16(6), 720-726. https://doi.org/10.1016/S1674-4799(10)60019-2.
  14. Dutta, S., Samui, P. and Kim, D. (2018), "Comparison of machine learning techniques to predict compressive strength of concrete", Comput. Concrete, 21(4), 463-470. https://doi.org/10.12989/cac.2018.21.4.463.
  15. Erdal, H., Erdal, M., Simsek, O. and Erdal, H. I. (2018), "Prediction of concrete compressive strength using nondestructive test results", Comput. Concrete, 21(4), 407-417. https://doi.org/10.12989/cac.2018.21.4.407.
  16. He, J., Jie, Y., Zhang, J., Yu, Y. and Zhang, G. (2013), "Synthesis and characterization of red mud and rice husk ash-based geopolymer composites", Cement Concrete Compos., 37, 108-118. https://doi.org/10.1016/j.cemconcomp.2012.11.010.
  17. Huseien, G.F., Mirza, J., Ismail, M. and Hussin, M.W. (2016), "Influence of different curing temperatures and alkali activators on properties of ggbs geopolymer mortars containing fly ash and palm-oil fuel ash", Constr. Build. Mater., 125, 1229-1240. https://doi.org/10.1016/j.conbuildmat.2016.08.153.
  18. Jindal, B.B., Singhal, D., Sharma, S. and Parveen, (2018), "Enhancing mechanical and durability properties of geopolymer concrete with mineral admixture", Comput. Concrete, 21(3), 345-353. https://doi.org/10.12989/cac.2018.21.3.345.
  19. Katpady, D.N., Takewaka, K. and Yamaguchi, T. (2017), "Development of slag based shirasu geopolymer", Comput. Concrete, 20(1), 77-84. https://doi.org/10.12989/cac.2017.20.1.077.
  20. Kumar, S. and Kumar, R. (2011), "Mechanical activation of fly ash: effect on reaction, structure and properties of resulting geopolymer", Ceram. Int., 37(2), 533-541. https://doi.org/10.1016/j.ceramint.2010.09.038.
  21. Kurklu, G. (2016), "The effect of high temperature on the design of blast furnace slag and coarse fly ash-based geopolymer mortar", Compos. Part B: Eng., 92, 9-18. https://doi.org/10.1016/j.compositesb.2016.02.043.
  22. Kusbiantoro, A., Nuruddin, M.F., Shafiq, N. and Qazi, S.A. (2012), "The effect of microwave incinerated rice husk ash on the compressive and bond strength of fly ash based geopolymer concrete", Constr. Build. Mater., 36, 695-703. https://doi.org/10.1016/j.conbuildmat.2012.06.064
  23. Marin-Lopez, C., Araiza, J. R., Manzano-Ramirez, A., Avalos, J. R., Perez-Bueno, J., Muniz-Villareal, M., Ventura-Ramos, E. and Vorobiev, Y. (2009), "Synthesis and characterization of a concrete based on metakaolin geopolymer", Inorganic Mater., 45(12), 1429-1432. https://doi.org/10.1134/S0020168509120231.
  24. Mozumder, R.A., Laskar, A.I. and Hussain, M. (2017), "Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines", Constr. Build. Mater., 132, 412-424. https://doi.org/10.1016/j.conbuildmat.2016.12.012.
  25. Pacheco-Torgal, F., Castro-Gomes, J. and Jalali, S. (2008), "Alkali-activated binders: A review: Part 1. historical background, terminology, reaction mechanisms and hydration products", Constr. Build. Mater., 22(7), 1305-1314. https://doi.org/10.1016/j.conbuildmat.2007.10.015.
  26. Prem, P.R., Bharatkumar, B. and Iyer, N.R. (2013), "Influence of curing regimes on compressive strength of ultra high performance concrete", Sadhana, 38(6), 1421-1431. https://doi.org/10.1007/s12046-013-0159-8.
  27. Prem, P.R., Murthy, A.R. and Bharatkumar, B.H. (2015), "Influence of curing regime and steel fibres on the mechanical properties of UHPC", Mag. Concrete Res., 67(18), 988-1002. http://dx.doi.org/10.1680/macr.14.00333
  28. Prem, P.R., Murthy, A.R. and Verma, M. (2018a), "Theoretical modelling and acoustic emission monitoring of RC beams strengthened with UHPC", Constr. Build. Mater., 158, 670-682. https://doi.org/10.1016/j.conbuildmat.2017.10.063.
  29. Prem, P.R., Verma, M. and Ambily, P. (2018b), "Sustainable cleaner production of concrete with high volume copper slag", J. Clean. Prod., 193, 43-58. https://doi.org/10.1016/j.jclepro.2018.04.245.
  30. Prem, P.R., Verma, M., Murthy, A.R., Rajasankar, J. and Bharatkumar, B. (2017), "Numerical and theoretical modelling of low velocity impact on UHPC panels", Struct. Eng. Mech., 63(2), 207-215. https://doi.org/10.12989/sem.2017.63.2.207.
  31. Quinlan, J.R. (1993), "Combining instance-based and model-based learning", Proceedings of the Tenth International Conference on Machine Learning, 236-243.
  32. Senthamilselvi, P. and Palanisamy, T. (2018), "Experimental and analytical study on flexural behaviour of fly ash and paper sludge ash based geopolymer concrete", Comput. Concrete, 21(2), 157-166. https://doi.org/10.12989/cac.2018.21.2.157.
  33. Temuujin, J., Williams, R. and Van Riessen, A. (2009), "Effect of mechanical activation of fly ash on the properties of geopolymer cured at ambient temperature", J. Mater. Proc. Technol., 209(12), 5276-5280. https://doi.org/10.1016/j.jmatprotec.2009.03.016.
  34. Tibshirani, R. (1996), "Regression shrinkage and selection via the lasso", J. R. Stat. Soc., Ser. B (Method.), 58(1), 267-288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.
  35. Tipping, M.E. (2001), "Sparse bayesian learning and the relevance vector machine", J. Mach. Learn. Res., 1, 211-244.
  36. Vapnik, V. (2013), The Nature of Statistical Learning Theory, Springer Science & Business Media.
  37. Verma, M., Anandavalli, N., Prakash, A. and Rajasankar, J. (2019), "Laced steel- concrete composite sandwich system in flexure: Experiments, analysis and design", J. Sandw. Struct. Mater., 1099636219842286. https://doi.org/10.1177/1099636219842286.
  38. Verma, M., Prem, P.R., Rajasankar, J. and Bharatkumar, B. (2016a), "On low- energy impact response of ultra-high performance concrete (UHPC) panels", Mater. Des., 92, 853-865. https://doi.org/10.1016/j.matdes.2015.12.065.
  39. Verma, M., Rajasankar, J., Anandavalli, N., Prakash, A. and Iyer, N.R. (2015), "Fuzzy similarity approach for ranking and health assessment of towers based on visual inspection", Adv. Struct. Eng., 18(9), 1399-1414. https://doi.org/10.1260/1369-4332.18.9.1399.
  40. Verma, M., Thirumalaiselvi, A. and Rajasankar, J. (2016b), "Kernel-based mod- els for prediction of cement compressive strength", Neur. Comput. Appl., 28(1), 1083-1100. https://doi.org/10.1007/s00521-016-2419-0.
  41. Verrelst, J., Alonso, L., Camps-Valls, G., Delegido, J. and Moreno, J. (2012), "Re- trieval of vegetation biophysical parameters using gaussian process techniques", Geosci. Remote Sens., IEEE Tran. on, 50(5), 1832-1843. https://doi.org/10.1109/TGRS.2011.2168962
  42. Vijai, K., Kumutha, R. and Vishnuram, B. (2015), "Flexural behaviour of fibre reinforced geopolymer concrete composite beams", Comput. Concrete, 15(3), 437-459. https://doi.org/10.12989/cac.2015.15.3.437.
  43. Williams, C.K. (1998), "Prediction with gaussian processes: From linear regression to linear prediction and beyond", Learning in Graphical Models, Springer.
  44. Williams, C.K. and Rasmussen, C.E. (2006), Gaussian Processes for Machine Learning, Vol. 2, No. 3, MIT Press, Cambridge, MA.
  45. Yadollahia, M.M. and Benli, A. (2017), "Stress-strain behavior of geopolymer under uniaxial compression", Comput. Concrete, 20(4), 381-389. https://doi.org/10.12989/cac.2017.20.4.381.
  46. Zhang, M., El-Korchi, T., Zhang, G., Liang, J. and Tao, M. (2014), "Synthesis factors affecting mechanical properties, microstructure, and chemical com- position of red mud-fly ash based geopolymers", Fuel, 134, 315-325. https://doi.org/10.1016/j.fuel.2014.05.058.
  47. Zou, H. and Hastie, T. (2005), "Regularization and variable selection via the elastic net", J. R. Stat. Soc.: Ser. B (Stat. Method.), 67(2), 301-320. https://doi.org/10.1111/j.1467-9868.2005.00503.x.